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Autonomous Agent

AI involvement: Multiple specialized agents execute a full pipeline — research, write, edit, review, publish — with human approval at one gate.

What This Workflow Type Is

An autonomous agent workflow is one where AI handles the entire process — from research to final deliverable — with minimal human intervention. This example is built using Claude Code subagents — specialized AI assistants that each run in their own context with domain expertise, coordinated by Claude Code as the orchestrator. The human sets the goal and reviews the draft at one checkpoint. Everything else runs autonomously.

At a Glance

  • AI involvement: Full — multiple agents plan, research, write, edit, and publish
  • Human oversight: One review gate between editing and publishing
  • Best for: Research-driven content production, multi-step pipelines with specialist roles
  • Complexity: High — multi-agent orchestration with a skill, a hook, and tool use

Characteristics

  • Multi-agent — different agents handle different stages, each with domain expertise
  • Pipeline-structured — output from one agent becomes input to the next
  • Skill-enhanced — the editor agent loads the editing-hbr-articles skill to apply codified editorial standards during its editing pass
  • Self-reviewing — the editor agent applies quality criteria from that skill before the human sees the draft
  • Gate-controlled — a built-in safety mechanism (called a "hook") automatically pauses the pipeline for human review before publishing
  • End-to-end — produces a finished deliverable (PDF + markdown) from a single goal statement

When to Use

Use autonomous agent workflows when the task:

  • Requires multiple distinct capabilities (research, writing, editing, formatting)
  • Follows a pipeline where each stage has clear inputs and outputs
  • Benefits from specialist expertise at each stage
  • Produces a deliverable that should meet professional standards
  • Can include a human review gate without breaking the flow

Example Scenario

The problem: A business leader wants to publish an HBR-style article about companies successfully using AI agents. The process requires deep research (finding real case studies with quantified outcomes), executive-level writing (translating technical concepts for business audiences), rigorous editing (applying HBR editorial standards), and professional publishing (PDF formatting with SEO metadata). Doing this manually involves multiple skill sets and takes days of focused work.

The solution: A multi-agent pipeline in Claude Code. One prompt triggers a chain of specialized agents — a researcher finds case studies, a writer produces the article, an editor applies HBR standards, the human reviews the draft, and a publisher formats the final deliverable. Each agent brings domain expertise that would otherwise require a different person.

The Goal Prompt

This single prompt triggers the entire pipeline:

"Please write an analysis and Harvard Business Review-style article on successful companies that you can find by doing research that have successfully used and applied AI agents to their business. This article is for a business leadership audience, and I'd like to have the final deliverable as a PDF, and markdown file."

Building Blocks

All building blocks are already included in the business-first-ai plugin — no additional installation required.

Building Block Type Role in Pipeline Source
ai-productivity-researcher Agent Finds documented case studies of companies using AI with quantified outcomes View on GitHub
tech-executive-writer Agent Writes the article for a business leadership audience View on GitHub
hbr-editor Agent Edits the draft against HBR editorial standards View on GitHub
editing-hbr-articles Skill Provides editorial criteria and cut/replace patterns for the editor View on GitHub
hbr-publisher Agent Formats the approved article as PDF and markdown with SEO metadata View on GitHub

How It Works

graph TD
    A["Goal prompt"] --> B["Claude Code<br>(orchestrator)"]
    B --> C["ai-productivity-researcher<br>finds case studies"]
    C --> D["tech-executive-writer<br>drafts the article"]
    D --> E["hbr-editor<br>+ editing-hbr-articles skill"]
    E --> F{"SubagentStop Hook"}
    F --> G["Human reviews draft"]
    G -->|Approved| H["hbr-publisher<br>formats PDF + markdown"]
    G -->|Rejected| I(("Stop"))
    H --> J["Final deliverables:<br>PDF + markdown article"]

Step-by-step:

  1. User provides the goal — a single prompt describing the article topic, audience, and desired deliverables.
  2. ai-productivity-researcher runs — searches news outlets, business publications, and analyst reports for documented case studies of companies using AI agents. Prioritizes HBR-caliber sources with quantified outcomes (revenue impact, productivity gains, cost savings).
  3. tech-executive-writer runs — takes the research output and produces a full-length article. Translates technical AI concepts for a non-technical business audience. Structures the piece with a compelling narrative, specific examples, and executive-level insights.
  4. hbr-editor runs — reads the editing-hbr-articles skill to load editorial criteria, then edits the draft. Checks structure (does the opening hook?), evidence quality (are claims supported by named companies and data?), voice (active, no hedging), and length (2,500-3,500 words for features). Makes direct, prescriptive edits.
  5. Pipeline pauses for review — a hook (an automatic rule in Claude Code that triggers at a specific point) stops the pipeline and presents the edited draft to the human.
  6. Human reviews — reads the edited article and either approves it to continue or stops the pipeline for manual revision.
  7. hbr-publisher runs (on approval) — formats the article for web publication (SEO metadata, social snippets) and generates a professional PDF. Produces two files: a markdown version and a PDF.

The Pipeline in Detail

Stage Agent/Component Input Output What Makes It Autonomous
Research ai-productivity-researcher Goal prompt Structured case study briefs Agent decides which sources to search and which cases meet the quality bar
Writing tech-executive-writer Research briefs Full article draft Agent structures the narrative, chooses which cases to feature, and adapts tone for the audience
Editing hbr-editor + skill Article draft Edited draft with tracked changes Agent applies codified editorial criteria — not subjective taste, but documented standards
Review gate SubagentStop hook Edited draft Human approval or rejection Pipeline pauses automatically — human decides quality, not the AI
Publishing hbr-publisher Approved draft PDF + markdown files Agent handles formatting, metadata, and layout without human input

Why Multiple Agents Instead of One?

Each agent is a specialist. The researcher knows where to find credible business case studies. The writer knows how to structure executive-level content. The editor knows HBR's specific editorial standards (loaded from a skill file with reference criteria). The publisher knows formatting and SEO.

A single generalist prompt could attempt all of this, but the output quality degrades because no single prompt can encode deep expertise across research methodology, executive writing style, editorial standards, and publication formatting. Splitting into specialists lets each agent focus on what it does best.

The Human Review Gate

The human review gate is critical. A "hook" — an automatic rule you configure in Claude Code — fires after the editor finishes and before the publisher starts, giving the human a chance to:

  • Approve — the article meets standards, continue to publishing
  • Reject — the article needs changes the AI can't make (factual corrections, strategic adjustments, tone shifts)

This is a deliberate design choice. The pipeline is autonomous enough to produce a near-final draft without human involvement, but publishing is a high-stakes action — once an article goes out, it represents the author. The gate ensures a human makes that call.

Usage

All five agents and the editing skill are included in the business-first-ai plugin.

# Install the plugin (one time)
/plugin install business-first-ai@handsonai

Then provide the goal prompt:

"Please write an analysis and Harvard Business Review-style article on successful companies that you can find by doing research that have successfully used and applied AI agents to their business. This article is for a business leadership audience, and I'd like to have the final deliverable as a PDF, and markdown file."

Claude Code orchestrates the full pipeline automatically. You'll be prompted to review the draft at the human-in-the-loop gate before publishing proceeds.

Customize the topic

Swap the article topic for anything relevant to a business audience. The pipeline structure stays the same — only the research and writing content changes:

  • "Write an HBR-style article about how mid-market companies are using AI to reduce customer churn"
  • "Research and write a thought leadership piece on AI-driven supply chain optimization"
  • "Produce a business article analyzing how professional services firms are adopting AI agents"

This pipeline is designed for Claude Code's multi-agent orchestration. Without it, you can still replicate the workflow manually by running each stage as a separate conversation:

  1. Research stage — In any AI tool with web search, prompt: "Find 5-7 documented case studies of companies successfully using AI agents, with quantified business outcomes. Prioritize sources from HBR, McKinsey, Forrester, or major business publications."
  2. Writing stage — In a new conversation, paste the research output and prompt: "Write a 2,500-3,500 word article for a business leadership audience about these companies' AI agent implementations. Use an HBR editorial style."
  3. Editing stage — In a new conversation, paste the draft and prompt: "Edit this article to HBR publication standards. Focus on: opening hook, evidence quality, active voice, and cutting redundancy."
  4. Review — Read the edited draft yourself.
  5. Publishing — Format manually or use your preferred publishing tools.

The manual approach works but loses the seamless handoff between agents, the codified editorial standards from the skill file, and the automatic human review gate.

Adapting This Example

The HBR article pipeline is one application, but the multi-agent orchestration pattern applies to any workflow where different stages require different expertise:

  • Client deliverable pipeline — researcher gathers data → analyst produces insights → writer creates the report → reviewer checks quality → designer formats the final document
  • Sales proposal generation — researcher profiles the prospect → writer drafts the proposal → pricing specialist adds numbers → reviewer ensures accuracy → formatter produces the PDF
  • Course content creation — researcher gathers source material → instructional designer structures the lesson → writer creates slides and exercises → editor reviews for clarity → publisher formats for the LMS
  • Competitive intelligence reports — scanner monitors competitor channels → analyst identifies key changes → writer summarizes findings → editor ensures accuracy → distributor sends to stakeholders

To adapt: identify the distinct stages of your workflow and the specialist expertise each stage requires. If you'd assign different people to different stages in a team setting, those stages are candidates for different agents.